Carrier relevance study for indoor localization using GSM
A study is made of subsets of relevant GSM carriers for an indoor localization problem. A database was created containing power measurement scans of all available GSM carriers in 5 of 8 rooms of a second storey laboratory in central Paris, France, and a statistical learning algorithm developed to di...
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| creator | Ahriz, I Oussar, Y Denby, B Dreyfus, Gérard |
| description | A study is made of subsets of relevant GSM carriers for an indoor localization problem. A database was created containing power measurement scans of all available GSM carriers in 5 of 8 rooms of a second storey laboratory in central Paris, France, and a statistical learning algorithm developed to discriminate between rooms based on these carrier strengths. To optimize the system, carrier relevance was ranked using either Orthogonal Forward Regression or Support Vector Machine - Recursive Feature Elimination procedures, and a subset of relevant variables obtained with cross-validation. Results show that the 60 most relevant carriers are sufficient to correctly localize 97% of scans in an independent test set. |
| doi_str_mv | 10.1109/WPNC.2010.5650492 |
| format | Conference Proceeding |
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A database was created containing power measurement scans of all available GSM carriers in 5 of 8 rooms of a second storey laboratory in central Paris, France, and a statistical learning algorithm developed to discriminate between rooms based on these carrier strengths. To optimize the system, carrier relevance was ranked using either Orthogonal Forward Regression or Support Vector Machine - Recursive Feature Elimination procedures, and a subset of relevant variables obtained with cross-validation. 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A database was created containing power measurement scans of all available GSM carriers in 5 of 8 rooms of a second storey laboratory in central Paris, France, and a statistical learning algorithm developed to discriminate between rooms based on these carrier strengths. To optimize the system, carrier relevance was ranked using either Orthogonal Forward Regression or Support Vector Machine - Recursive Feature Elimination procedures, and a subset of relevant variables obtained with cross-validation. Results show that the 60 most relevant carriers are sufficient to correctly localize 97% of scans in an independent test set.</description><subject>Classification algorithms</subject><subject>Fingerprint recognition</subject><subject>GSM</subject><subject>GSM networks</subject><subject>Indoor localization</subject><subject>Laboratories</subject><subject>Support vector machine classification</subject><subject>Training</subject><subject>variable selection</subject><isbn>1424471583</isbn><isbn>9781424471577</isbn><isbn>1424471575</isbn><isbn>9781424471584</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj81KxDAUheNCUMd5AHGTF-h4b5ImzVKKjsL4Aw64HNLkRiK1laQjjE9vwVl9HDh8nMPYFcIKEezN--tzuxIwx1rXoKw4YReohFIG60aesWUpnwCARmuJ4pzZ1uWcKPNMPf24wRMv0z4ceBwzT0MYZ_Sjd336dVMaB74vafjg67enS3YaXV9oeeSCbe_vtu1DtXlZP7a3mypZmKoumODRGGwMWoMgKHSia2qIEHQ0Ubmg9LzUKC_ruadBGRmInPDRdcLKBbv-1yYi2n3n9OXyYXc8J_8AV8ZEpQ</recordid><startdate>201003</startdate><enddate>201003</enddate><creator>Ahriz, I</creator><creator>Oussar, Y</creator><creator>Denby, B</creator><creator>Dreyfus, Gérard</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201003</creationdate><title>Carrier relevance study for indoor localization using GSM</title><author>Ahriz, I ; Oussar, Y ; Denby, B ; Dreyfus, Gérard</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-bd7dc177187197102edb2b850f0d6f7f4ad4665074c3517760473deea2cfab293</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Classification algorithms</topic><topic>Fingerprint recognition</topic><topic>GSM</topic><topic>GSM networks</topic><topic>Indoor localization</topic><topic>Laboratories</topic><topic>Support vector machine classification</topic><topic>Training</topic><topic>variable selection</topic><toplevel>online_resources</toplevel><creatorcontrib>Ahriz, I</creatorcontrib><creatorcontrib>Oussar, Y</creatorcontrib><creatorcontrib>Denby, B</creatorcontrib><creatorcontrib>Dreyfus, Gérard</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Ahriz, I</au><au>Oussar, Y</au><au>Denby, B</au><au>Dreyfus, Gérard</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Carrier relevance study for indoor localization using GSM</atitle><btitle>2010 7th Workshop on Positioning, Navigation and Communication</btitle><stitle>WPNC</stitle><date>2010-03</date><risdate>2010</risdate><spage>168</spage><epage>173</epage><pages>168-173</pages><eisbn>1424471583</eisbn><eisbn>9781424471577</eisbn><eisbn>1424471575</eisbn><eisbn>9781424471584</eisbn><abstract>A study is made of subsets of relevant GSM carriers for an indoor localization problem. A database was created containing power measurement scans of all available GSM carriers in 5 of 8 rooms of a second storey laboratory in central Paris, France, and a statistical learning algorithm developed to discriminate between rooms based on these carrier strengths. To optimize the system, carrier relevance was ranked using either Orthogonal Forward Regression or Support Vector Machine - Recursive Feature Elimination procedures, and a subset of relevant variables obtained with cross-validation. Results show that the 60 most relevant carriers are sufficient to correctly localize 97% of scans in an independent test set.</abstract><pub>IEEE</pub><doi>10.1109/WPNC.2010.5650492</doi><tpages>6</tpages></addata></record> |
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| subjects | Classification algorithms Fingerprint recognition GSM GSM networks Indoor localization Laboratories Support vector machine classification Training variable selection |
| title | Carrier relevance study for indoor localization using GSM |
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